Experimental and Statistical Analysis of Iron Powder for Green Heat Production

Abstract

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle’s cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles.